Use S3 storage instead of /data folder

[bonk1t]

In the WriteAndSaveProgram skill, we utilize the agency_id to construct a directory path for each agency. However, this is a bad practice and we need a persistent solution. We should establish an external storage solution for each user. Using S3-compliant storage and creating a bucket would be a viable approach.

To integrate S3 storage into our FastAPI application, we can follow these steps:

1. [us] Choose an S3 Storage Provider: Since we've deployed the app on Heroku, supporting Amazon S3 for storage would be a straightforward choice due to its broad support, scalability, and integration with Heroku.

2. [end user] Set Up the S3 Bucket: Set up an S3 bucket in our preferred region. Ensure the bucket policy and IAM roles are set up accordingly to allow programmatic access.

3. [us] Install Boto3: Boto3 is the Amazon Web Services (AWS) SDK for Python, which allows Python developers to write software that uses services like Amazon S3.

4. [end user] Configure Access Credentials: End users need to securely store their access credentials (such as AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY) in user variables.

5. [us] Modify the Current File Handling Logic: We currently have the WriteAndSaveProgram skill in /backend/custom_skills/write_and_save_program.py that manages file writing locally. We'll replace the logic in the File class's run method with code to upload files to S3. Here's a basic example of how we might upload files to S3:

import boto3
from botocore.exceptions import NoCredentialsError

def upload_to_s3(bucket_name, file_name, body):
    s3 = boto3.client('s3')
    try:
        s3.put_object(Bucket=bucket_name, Key=file_name, Body=body)
        print(f"File {file_name} uploaded to {bucket_name}")
    except NoCredentialsError:
        print("Credentials not available")

# In the File class's run method, replace the file writing logic with:
upload_to_s3("bucket_name", self.file_name, self.body)

6. [us] Test the Integration: Before deploying the changes to production, ensure we've tested the S3 integration locally and on a staging environment to confirm that files are being uploaded correctly, and our application can retrieve them as needed.

7. [us] Update Documentation: Reflect these changes in both the main README.md and the backend's README.md, specifying the requirement for S3 storage and environment variables setup.

Use this branch:
https://github.com/AI-in-Hand/platform/tree/feat/use_s3_storage_instead_of_data_folder
Need to apply the new approach to all the skills that read/write to the local drive.